US12566927B2ActiveUtilityA1

Dialogue processing apparatus, learning apparatus, dialogue processing method, learning method and program

77
Assignee: NTT INCPriority: May 28, 2019Filed: May 22, 2024Granted: Mar 3, 2026
Est. expiryMay 28, 2039(~12.9 yrs left)· nominal 20-yr term from priority
G06V 30/41G06F 40/56G06F 16/90G06F 40/35
77
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Cited by
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References
7
Claims

Abstract

A generation unit that takes a question Qi that is a word sequence representing a current question in a dialogue, a document P used to generate an answer Ai to the question Qi, a question history {Qi-1, . . . , Qi-k} that is a set of word sequences representing k past questions, and an answer history {Ai-1, . . . , Ai-k} that is a set of word sequences representing answers to the k questions as inputs, and generates the answer Ai by machine reading comprehension in an extractive mode or a generative mode using pre-trained model parameters is provided.

Claims

exact text as granted — not AI-modified
The invention claimed is: 
     
         1 . A dialogue processing apparatus comprising:
 a hardware processor; and   a storage storing therein a set of instructions that, when executed by the hardware processor, causes the dialogue processing apparatus to:   receive as inputs:
 a word sequence representing a current question in a dialogue; 
 text to be used to generate an answer to the current question; 
 a question history that is a set of word sequences representing past questions; and 
 an answer history that is a set of word sequences representing answers to the past questions; and 
   generate the answer to the current question, by extractive or generative machine reading comprehension, using pre-trained model parameters.   
     
     
         2 . The dialogue processing apparatus according to  claim 1 ,
 wherein the hardware processor further causes the dialogue processing apparatus to calculate features related to the question history and features related to the answer history in the text, and   wherein the features calculated with respect to the question history and the features calculated with respect to the answer history in the text are incorporated in features in the text that are used to generate the answer to the current question.   
     
     
         3 . The dialogue processing apparatus according to  claim 2 , wherein the hardware processor further causes the dialogue processing apparatus to:
 calculate features related to the current question in the text;   combine the features related to the current question, the features related to the question history, and the features related to the answer history in the text; and   generate the answer to the current question, by extractive or generative machine reading comprehension, based on the features in the text that combine the features of and reflect the current question, the question history, and the answer history, and that are used to generate the answer to the current question.   
     
     
         4 . A learning apparatus comprising:
 a hardware processor; and   a storage storing therein a set of instructions that, when executed by the hardware processor, causes the learning apparatus to:   receive as inputs:
 a word sequence representing a current question in a dialogue; 
 text to be used to generate an answer to the current question; 
 a question history that is a set of word sequences representing past questions; and 
 an answer history that is a set of word sequences representing answers to the past questions; 
   generate the answer to the current question, by extractive or generative machine reading comprehension, using model parameters; and   update the model parameters, by supervised learning, using the generated answer and a correct answer to the current question.   
     
     
         5 . A dialogue processing method to be executed by a computer, the method comprising:
 receiving as inputs:
 a word sequence representing a current question in a dialogue; 
 text to be used to generate an answer to the current question; 
 a question history that is a set of word sequences representing past questions; and 
 an answer history that is a set of word sequences representing answers to the past questions; and 
   generating the answer to the current question, by extractive or generative machine reading comprehension, using pre-trained model parameters.   
     
     
         6 . A non-transitory computer-readable recording medium storing therein a set of instructions that, when executed by a computer, causes the computer to operate as the dialogue processing apparatus of  claim 1 . 
     
     
         7 . A non-transitory computer-readable recording medium storing therein a set of instructions that, when executed by a computer, causes the computer to operate as the learning apparatus of  claim 4 .

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